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Most organizations aim to be AI-forward, but their legacy infrastructure may already be costing them the race.<\/em><\/p>\n Ask senior executives what is slowing AI progress, and they will cite talent, data quality, change management or regulation. What is often overlooked is the underlying IT infrastructure.<\/p>\n Matthias Patzak, Executive in Residence at AWS and former CTO, highlights two striking figures: 70% of enterprise workloads still run on servers owned and managed by the organization, while 70% of the world\u2019s largest companies operate software that is more than 20 years old.<\/p>\n AI may be the decisive competitive factor, but most organizations remain constrained by infrastructure that struggles to support it.<\/p>\n \u201cWe are at an inflection point,\u201d says Patzak. \u201cLeveraging AI requires proper data infrastructure and clean architecture. Organizations must act now to migrate and modernize at scale to become AI-first.\u201d<\/p>\n Until recently, enterprise modernization at scale felt insurmountable\u2014too many applications, overwhelming complexity and business risk if things go wrong.<\/p>\n Within AWS\u2019 broader migration and modernization framework, AWS Transform<\/a> is the first agentic AI service that accelerates the transformation of full-stack Windows, mainframe, VMware workloads, as well as custom transformation of code, APIs, frameworks and more. <\/p>\n Where IT teams once spent months manually assessing workloads, mapping dependencies and analyzing infrastructure, AI agents now handle these tasks in hours. In some cases, VMware migration planning that took weeks can now be completed in minutes, compressing the early phases of transformation.<\/p>\n The acceleration at the front end translates into faster overall delivery. With AWS Transform, organizations are modernizing full-stack Windows applications up to five times faster, while early adopters have reduced end-to-end migration timelines by as much as 50%. But speed is just the tip of the iceberg.<\/p>\n \u201cWe’re seeing organizations cut transformation costs by 40% to 60% with AWS Transform, freeing up budget that once went to maintaining legacy systems and redirecting it toward innovation and customer experience,\u201d says Patzak.<\/p>\n Modernization must deliver clear ROI through reduced costs and improved efficiency but protecting critical workloads is equally paramount. Organizations migrating to AWS experience 45% fewer security incidents while strengthening their overall security posture, Patzak adds. <\/p>\n Thomson Reuters ran hundreds of mission-critical applications that were stable but increasingly constraining innovation and costly to maintain.<\/p>\n Joel Hron, CTO, Thomson Reuters<\/span><\/p>\n Thomson Reuters<\/small><\/div>\n<\/div>\n<\/figure>\n \u201cAt our scale, legacy infrastructure becomes a business constraint, not just a technical one,\u201d says Joel Hron, CTO of Thomson Reuters. \u201cThose systems limited how efficiently we could operate and how quickly we could innovate.\u201d<\/p>\n The decision to modernize went beyond efficiency or cost. It was fundamentally about positioning the organization to build and scale AI-driven products. \u201cYou cannot build modern, AI-powered products on top of fragmented legacy systems,\u201d Hron notes.<\/p>\n \u201cOur partnership with AWS has been central to executing this transformation at scale,\u201d he adds. <\/p>\n To deliver on that transformation, Thomson Reuters used AWS Transform to replace slow, traditional modernization approaches with a faster, AI-assisted model, allowing teams to work in parallel to map dependencies, plan migrations and automate code transformation.<\/p>\n Thomson Reuters is now modernizing approximately 1.5 million lines of code per month and has reduced cloud operating expenses by 30%, while also accelerating its software development lifecycle.<\/p>\n \u201cModernization is what made our AI strategy real,\u201d Hron says. \u201cIt created the foundation required to deliver a different category of AI\u2014one built for work professionals can stand behind.\u201d<\/p>\n By modernizing through AWS Transform, Thomson Reuters has built the foundation to deliver fiduciary-grade AI through CoCounsel, designed for high-stakes professional work where accuracy, accountability and trusted sources are essential.<\/p>\n For BMW, the challenge was not aging infrastructure but growth outpacing what even a well-run, on-premises environment could support. The company was seeing exponential expansion across its value chain: from connected vehicles to over-the-air updates to AI-driven engineering and manufacturing.<\/p>\n \u201cOver the past years, BMW\u2019s infrastructure was pushed to its limits\u2014not because it was outdated but because the scale and speed of our digital ambition had reached a new level,\u201dsays Frank Uslaub, VP of Transformation at BMW Group IT. \u201cEven highly optimized on-premises environments, including mainframe\u2011based systems, are not designed for elastic scaling, rapid innovation cycles or AI-native workloads at this magnitude.\u201d<\/p>\n The shift was driven by a clear realization. \u201cAI fundamentally changes how infrastructure must be designed and how modernization must be executed,\u201d he says. \u201cAI is both a driver\u2014raising expectations around speed, data availability and intelligence\u2014and an enabler, helping us modernize complex landscapes more efficiently and reliably.\u201d This is a shift that Uslaub has seen firsthand.<\/p>\n The all-new BMW iX3 marks the dawn of the Neue Klasse era\u2014fully electric, up to 500 miles of range.<\/span><\/p>\n BMW AG<\/small><\/div>\n<\/div>\n<\/figure>\n \u201cWorking with AWS has fundamentally changed how BMW turns AI ambition and digital transformation into execution,\u201d he adds. Cloud-native platforms remove infrastructure lead times from innovation, enabling faster iteration and deployment at scale. \u201cTeams can experiment, modernize and scale solutions globally much faster, shifting from isolated initiatives to continuous delivery at enterprise scale,\u201d Uslaub says. <\/p>\n This operating model is translating into tangible outcomes. By increasing resilience and predictability in mission-critical systems, AWS Transform\u2019s agentic AI enables BMW to scale with confidence globally.<\/p>\n Equally significant is the strategic groundwork being laid. <\/p>\n \u201cWe are building AI-based transformation as a core capability,\u201d Uslaub says. \u201cBMW is positioned not just to adopt AI but to continuously transform with AI\u2014a decisive differentiator in a software\u2011defined world.\u201d<\/p>\n In the AI era, the organizations that lead will not be defined by talent and investment alone but by the infrastructure that turns strategy into delivered products.<\/p>\n \u201cThe biggest mistake,\u201d says Hron, \u201cis treating modernization as a cost decision instead of a strategic one.\u201d <\/p>\n Legacy systems create friction that slows innovation, increases operational burden and makes it harder to adopt new technologies, often at a greater cost than organizations anticipate. Fortunately, migration and modernization efforts that once took years can be accelerated dramatically through AWS Transform. <\/p>\n To learn more about AWS Migration & Modernization and request a complimentary Optimization and Licensing Assessment<\/a>, visit aws.com<\/a>.<\/p>\n<\/div>\nAWS Transform: The Fast Track To An AI-Ready Future<\/h3>\n
From Migration To Modernization: How Thomson Reuters Propelled Its AI Transformation<\/h3>\n
Geared For Speed, Built For The Long Haul: BMW\u2019s AI Engine<\/h3>\n
The Moment Of Reckoning For Enterprise Leaders<\/h3>\n